A new anonymity model for privacy-preserving data publishing
Xuezhen Huang, Jiqiang Liu, Zhen Han, Jun Yang · China Communications · 2014
Privacy-preserving data publishing (PPDP) is one of the hot issues in the field of the network security. The existing PPDP technique cannot deal with generality attacks, which explicitly contain the sensitivity attack and the similarity attack. This paper proposes a novel model, (w, γ, k)-anonymity, to avoid generality attacks on both cases of numeric and categorical attributes. We show that theoptimal (w, γ, k)-anonymity problemis NP-hard and conduct the Top-down Local recoding (TDL) algorithm to implement the model. Our experiments validate the improvement of our model with real data.